import sys

!{sys.executable} --version

Python 3.7.9

import necessary libraries

import pandas as pd
import numpy as np
import plotly.express as px
import plotly
import plotly.graph_objs as go
from plotly import tools
from plotly.offline import init_notebook_mode, plot, iplot

Load Data

This data will be renewed day by day

data = pd.read_csv('https://raw.githubusercontent.com/datasets/covid-19/master/data/countries-aggregated.csv')
data.head()

	Date	Country	Confirmed	Recovered	Deaths
0	2020-01-22	Afghanistan	0	0	0
1	2020-01-23	Afghanistan	0	0	0
2	2020-01-24	Afghanistan	0	0	0
3	2020-01-25	Afghanistan	0	0	0
4	2020-01-26	Afghanistan	0	0	0

data.tail(10)

	Date	Country	Confirmed	Recovered	Deaths
82358	2021-03-16	Zimbabwe	36535	34124	1507
82359	2021-03-17	Zimbabwe	36552	34136	1508
82360	2021-03-18	Zimbabwe	36611	34226	1509
82361	2021-03-19	Zimbabwe	36652	34249	1510
82362	2021-03-20	Zimbabwe	36662	34257	1510
82363	2021-03-21	Zimbabwe	36665	34269	1512
82364	2021-03-22	Zimbabwe	36684	34315	1514
82365	2021-03-23	Zimbabwe	36717	34447	1516
82366	2021-03-24	Zimbabwe	36749	34476	1516
82367	2021-03-25	Zimbabwe	36778	34555	1518

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 82368 entries, 0 to 82367
Data columns (total 5 columns):
 #   Column     Non-Null Count  Dtype 
---  ------     --------------  ----- 
 0   Date       82368 non-null  object
 1   Country    82368 non-null  object
 2   Confirmed  82368 non-null  int64 
 3   Recovered  82368 non-null  int64 
 4   Deaths     82368 non-null  int64 
dtypes: int64(3), object(2)
memory usage: 3.1+ MB

data.describe()

	Confirmed	Recovered	Deaths
count	8.236800e+04	8.236800e+04	82368.000000
mean	2.015106e+05	1.184990e+05	5096.977455
std	1.194945e+06	6.261371e+05	24804.586512
min	0.000000e+00	0.000000e+00	0.000000
25%	1.320000e+02	2.600000e+01	1.000000
50%	4.236500e+03	1.976000e+03	74.000000
75%	5.690275e+04	3.000775e+04	976.250000
max	3.007928e+07	1.126464e+07	546822.000000

Visualiize the data with plotly

fig = px.choropleth(data,locations='Country',locationmode='country names',color='Confirmed',animation_frame='Date')
fig.update_layout(title='Choropleth Map of Confirmed Cases -till today',template="plotly_dark")
fig.show()

fig = px.choropleth(data,locations='Country',locationmode='country names',color='Confirmed',animation_frame='Date',scope='africa')
fig.update_layout(title='Choropleth Map of Confirmed Cases - Africa till today',template="plotly_dark")
fig.show()

Spread over Time

fig = px.scatter_geo(data,locations='Country',locationmode='country names',color='Confirmed',size='Confirmed',hover_name="Country",animation_frame='Date',title='Spread over Time')
fig.update(layout_coloraxis_showscale=True,layout_template="plotly_dark")
fig.show()

fig = px.choropleth(data,locations='Country',locationmode='country names',color='Recovered',animation_frame='Date')
fig.update_layout(title='Choropleth Map of Recovered Cases -till today',template="plotly_dark")
fig.show()

fig = px.scatter_geo(data,locations='Country',locationmode='country names',color='Recovered',size='Recovered',hover_name="Country",animation_frame='Date',title='Recovery over Time')
fig.update(layout_coloraxis_showscale=True,layout_template="plotly_dark")
fig.show()

fig = px.choropleth(data,locations='Country',locationmode='country names',color='Deaths',animation_frame='Date')
fig.update_layout(title='Choropleth Map of Deaths -till today',template="plotly_dark")
fig.show()

fig = px.scatter_geo(data,locations='Country',locationmode='country names',color='Deaths',size='Deaths',hover_name="Country",animation_frame='Date',title='Deaths over Time')
fig.update(layout_coloraxis_showscale=True,layout_template="plotly_dark")
fig.show()

extract latitudes & longtidues of locations

import geopy
from geopy.geocoders import Nominatim

geolocator=Nominatim(user_agent="app")

location = geolocator.geocode("Egypt")
print(location.latitude, location.longitude)

26.2540493 29.2675469

# copy the data to make changing
df = data.copy()

df

	Date	Country	Confirmed	Recovered	Deaths
0	2020-01-22	Afghanistan	0	0	0
1	2020-01-23	Afghanistan	0	0	0
2	2020-01-24	Afghanistan	0	0	0
3	2020-01-25	Afghanistan	0	0	0
4	2020-01-26	Afghanistan	0	0	0
...	...	...	...	...	...
82363	2021-03-21	Zimbabwe	36665	34269	1512
82364	2021-03-22	Zimbabwe	36684	34315	1514
82365	2021-03-23	Zimbabwe	36717	34447	1516
82366	2021-03-24	Zimbabwe	36749	34476	1516
82367	2021-03-25	Zimbabwe	36778	34555	1518

82368 rows × 5 columns

df[df['Country']=='Egypt'].tail(10)

	Date	Country	Confirmed	Recovered	Deaths
23156	2021-03-16	Egypt	192195	148089	11384
23157	2021-03-17	Egypt	192840	148424	11431
23158	2021-03-18	Egypt	193482	148823	11472
23159	2021-03-19	Egypt	194127	149256	11512
23160	2021-03-20	Egypt	194771	149489	11557
23161	2021-03-21	Egypt	195418	149934	11598
23162	2021-03-22	Egypt	196061	150424	11637
23163	2021-03-23	Egypt	196709	150924	11680
23164	2021-03-24	Egypt	197350	151444	11720
23165	2021-03-25	Egypt	198011	151765	11768

# Know the maximum value 
df2=df.groupby(['Country'])[['Confirmed','Recovered','Deaths']].max().reset_index()

df2

	Country	Confirmed	Recovered	Deaths
0	Afghanistan	56226	49937	2467
1	Albania	122767	87760	2184
2	Algeria	116543	81065	3071
3	Andorra	11687	11111	114
4	Angola	21914	20190	532
...	...	...	...	...
187	Vietnam	2579	2265	35
188	West Bank and Gaza	232038	205289	2521
189	Yemen	3816	1580	810
190	Zambia	87318	83895	1191
191	Zimbabwe	36778	34555	1518

192 rows × 4 columns

df2[df2['Country']=='Egypt']

	Country	Confirmed	Recovered	Deaths
53	Egypt	198011	151765	11768

lat_lon=[]
geolocator=Nominatim(user_agent="app")
for location in df2['Country']:
    location = geolocator.geocode(location)
    if location is None:
        lat_lon.append(np.nan)
    else:    
        geo=(location.latitude,location.longitude)
        lat_lon.append(geo)

lat_lon

[(33.7680065, 66.2385139),
 (41.000028, 19.9999619),
 (28.0000272, 2.9999825),
 (42.5407167, 1.5732033),
 (-11.8775768, 17.5691241),
 (17.2234721, -61.9554608),
 (-34.9964963, -64.9672817),
 (40.7696272, 44.6736646),
 (-24.7761086, 134.755),
 (47.69641095, 14.754807384371395),
 (40.3936294, 47.7872508),
 (24.7736546, -78.0000547),
 (26.1551249, 50.5344606),
 (24.4768783, 90.2932426),
 (13.1500331, -59.5250305),
 (53.4250605, 27.6971358),
 (50.6402809, 4.6667145),
 (16.8259793, -88.7600927),
 (9.2231105, 2.31006719638123),
 (27.549511, 90.5119273),
 (-17.0568696, -64.9912286),
 (44.3053476, 17.5961467),
 (-23.1681782, 24.5928742),
 (-10.3333333, -53.2),
 (4.4137155, 114.5653908),
 (42.72572155, 25.336199594318522),
 (12.0753083, -1.6880314),
 (17.1750495, 95.9999652),
 (-3.3634357, 29.8870575),
 (16.0000552, -24.0083947),
 (13.5066394, 104.869423),
 (4.6125522, 13.1535811),
 (61.0666922, -107.991707),
 (7.0323598, 19.9981227),
 (15.6134137, 19.0156172),
 (-31.7613365, -71.3187697),
 (35.000074, 104.999927),
 (2.8894434, -73.783892),
 (-12.2045176, 44.2832964),
 (-0.7179165000000001, 16.018048574081842),
 (-2.9814344, 23.8222636),
 (9.5773243, -83.82371114343024),
 (7.9897371, -5.5679458),
 (45.5643442, 17.0118954),
 (23.0131338, -80.8328748),
 (34.9823018, 33.1451285),
 (49.8167003, 15.4749544),
 (55.670249, 10.3333283),
 (53.8953584, 27.5554078),
 (11.85677545, 42.757784519943655),
 (19.28131815, -70.035906834967),
 (19.28131815, -70.035906834967),
 (-1.3397668, -79.3666965),
 (26.2540493, 29.2675469),
 (13.8000382, -88.9140683),
 (1.613172, 10.5170357),
 (15.9500319, 37.9999668),
 (58.7523778, 25.3319078),
 (-26.5624806, 31.3991317),
 (10.2116702, 38.6521203),
 (-18.1239696, 179.0122737),
 (63.2467777, 25.9209164),
 (46.603354, 1.8883335),
 (-0.8999695, 11.6899699),
 (13.44294275, -16.118732162564726),
 (32.3293809, -83.1137366),
 (51.0834196, 10.4234469),
 (7.8573710000000005, -1.0840975468820433),
 (38.9953683, 21.9877132),
 (12.1360374, -61.6904045),
 (15.6356088, -89.8988087),
 (10.7226226, -10.7083587),
 (11.6688305, -15.535471371611191),
 (4.8417097, -58.6416891),
 (19.1399952, -72.3570972),
 (38.9247244, -77.06572732690151),
 (15.2572432, -86.0755145),
 (47.16115805, 19.135174733081314),
 (64.9841821, -18.1059013),
 (22.3511148, 78.6677428),
 (-2.4833826, 117.8902853),
 (32.6475314, 54.5643516),
 (33.0955793, 44.1749775),
 (52.865196, -7.9794599),
 (31.5313113, 34.8667654),
 (42.6384261, 12.674297),
 (18.1850507, -77.3947693),
 (36.5748441, 139.2394179),
 (31.1667049, 36.941628),
 (47.2286086, 65.2093197),
 (-0.1667169, 37.48603689110992),
 (36.638392, 127.6961188),
 (42.5653218, 20.913641374540397),
 (29.2733964, 47.4979476),
 (41.5089324, 74.724091),
 (20.0171109, 103.378253),
 (56.8406494, 24.7537645),
 (33.8750629, 35.843409),
 (-29.6039267, 28.3350193),
 (5.7499721, -9.3658524),
 (26.4215275, 17.30331995251014),
 (47.1594125, 9.546911556940856),
 (55.3500003, 23.7499997),
 (49.8158683, 6.1296751),
 (52.4387696, 4.8185293),
 (-18.9249604, 46.4416422),
 (-13.2687204, 33.9301963),
 (4.5693754, 102.2656823),
 (4.7064352, 73.3287853),
 (16.3700359, -2.2900239),
 (35.9446731, 14.383630615858262),
 (8.9995549, 168.0002575),
 (20.2540382, -9.2399263),
 (-20.2759451, 57.5703566),
 (22.5000485, -100.0000375),
 (8.6065, 152.00846930625),
 (47.2879608, 28.5670941),
 (43.73844905, 7.424224092532953),
 (46.8250388, 103.8499736),
 (42.9868853, 19.5180992),
 (31.1728205, -7.3362482),
 (-19.302233, 34.9144977),
 (-23.2335499, 17.3231107),
 (28.1083929, 84.0917139),
 (52.5001698, 5.7480821),
 (-41.5000831, 172.8344077),
 (12.6090157, -85.2936911),
 (17.7356214, 9.3238432),
 (9.6000359, 7.9999721),
 (41.6171214, 21.7168387),
 (60.5000209, 9.0999715),
 (21.0000287, 57.0036901),
 (30.3308401, 71.247499),
 (8.559559, -81.1308434),
 (-5.6816069, 144.2489081),
 (-23.3165935, -58.1693445),
 (-6.8699697, -75.0458515),
 (12.7503486, 122.7312101),
 (52.215933, 19.134422),
 (39.4332242, -8.567392135367742),
 (25.3336984, 51.2295295),
 (45.9852129, 24.6859225),
 (64.6863136, 97.7453061),
 (-1.9646631, 30.0644358),
 (17.250512, -62.6725973),
 (13.8250489, -60.975036),
 (12.90447, -61.2765569),
 (-13.7693895, -172.1200508),
 (43.9458623, 12.458306),
 (0.8875498, 6.9648718),
 (25.6242618, 42.3528328),
 (14.46517725, -14.765340959100413),
 (44.1534121, 20.55144),
 (-4.6574977, 55.4540146),
 (8.6400349, -11.8400269),
 (1.357107, 103.8194992),
 (48.6726975, 19.63679421037233),
 (46.149034549999996, 14.626325753340627),
 (-8.7053941, 159.1070693851845),
 (8.3676771, 49.083416),
 (-28.579397200000002, 24.086794125366062),
 (7.8699431, 29.6667897),
 (39.3260685, -4.8379791),
 (7.5554942, 80.7137847),
 (14.5844444, 29.4917691),
 (4.1413025, -56.0771187),
 (59.6749712, 14.5208584),
 (46.813331250000005, 8.444947437939408),
 (34.6401861, 39.0494106),
 (23.9739374, 120.9820179),
 (38.6281733, 70.8156541),
 (-6.5247123, 35.7878438),
 (14.8971921, 100.83273),
 (-8.5151979, 125.8375756),
 (8.7800265, 1.0199765),
 (10.8677845, -60.9821067),
 (33.994952999999995, 9.367097748325554),
 (38.9597594, 34.9249653),
 (39.7837304, -100.4458825),
 (1.3769751000000001, 32.72391099113675),
 (49.4871968, 31.2718321),
 (24.0002488, 53.9994829),
 (54.7023545, -3.2765753),
 (-32.8755548, -56.0201525),
 (41.32373, 63.9528098),
 (-16.5255069, 168.1069154),
 (8.0018709, -66.1109318),
 (13.2904027, 108.4265113),
 (31.9049661, 35.2023413),
 (16.3471243, 47.8915271),
 (-14.5189121, 27.5589884),
 (-19.01688, 29.35365015971339)]

df2['geo_loc']=lat_lon

lat,lon=zip(*np.array(df2['geo_loc']))

df2['lat']=lat
df2['lon']=lon

df2

	Country	Confirmed	Recovered	Deaths	geo_loc	lat	lon
0	Afghanistan	56226	49937	2467	(33.7680065, 66.2385139)	33.768006	66.238514
1	Albania	122767	87760	2184	(41.000028, 19.9999619)	41.000028	19.999962
2	Algeria	116543	81065	3071	(28.0000272, 2.9999825)	28.000027	2.999983
3	Andorra	11687	11111	114	(42.5407167, 1.5732033)	42.540717	1.573203
4	Angola	21914	20190	532	(-11.8775768, 17.5691241)	-11.877577	17.569124
...	...	...	...	...	...	...	...
187	Vietnam	2579	2265	35	(13.2904027, 108.4265113)	13.290403	108.426511
188	West Bank and Gaza	232038	205289	2521	(31.9049661, 35.2023413)	31.904966	35.202341
189	Yemen	3816	1580	810	(16.3471243, 47.8915271)	16.347124	47.891527
190	Zambia	87318	83895	1191	(-14.5189121, 27.5589884)	-14.518912	27.558988
191	Zimbabwe	36778	34555	1518	(-19.01688, 29.35365015971339)	-19.016880	29.353650

192 rows × 7 columns

df2.drop(['geo_loc'],axis=1,inplace=True)

df2

	Country	Confirmed	Recovered	Deaths	lat	lon
0	Afghanistan	56226	49937	2467	33.768006	66.238514
1	Albania	122767	87760	2184	41.000028	19.999962
2	Algeria	116543	81065	3071	28.000027	2.999983
3	Andorra	11687	11111	114	42.540717	1.573203
4	Angola	21914	20190	532	-11.877577	17.569124
...	...	...	...	...	...	...
187	Vietnam	2579	2265	35	13.290403	108.426511
188	West Bank and Gaza	232038	205289	2521	31.904966	35.202341
189	Yemen	3816	1580	810	16.347124	47.891527
190	Zambia	87318	83895	1191	-14.518912	27.558988
191	Zimbabwe	36778	34555	1518	-19.016880	29.353650

192 rows × 6 columns

import folium

folium.Map(zoom_start=2)

Make this Notebook Trusted to load map: File -> Trust Notebook

# Create a map
m = folium.Map(location=[54, 15], tiles='openstreetmap',zoom_start=2)

# Add points to the map
for id,row in df2.iterrows():
    folium.Marker(location=[row['lat'],row['lon']], popup=row['Confirmed']).add_to(m)

# Display the map
m

Make this Notebook Trusted to load map: File -> Trust Notebook

for id,row in df2.iterrows():
    folium.Marker(location=[row['lat'],row['lon']], popup=row['Recovered']).add_to(m)

# Display the map
m

Make this Notebook Trusted to load map: File -> Trust Notebook

m = folium.Map(location=[54, 15], tiles='openstreetmap', zoom_start=2)

# Add points to the map
for idx, row in df2.iterrows():
    folium.Marker([row['lat'], row['lon']], popup=row['Deaths']).add_to(m)

# Display the map
m

Make this Notebook Trusted to load map: File -> Trust Notebook

m = folium.Map(location=[54,15], tiles='cartodbpositron', zoom_start=2)

# Add points to the map
from folium.plugins import MarkerCluster
mc = MarkerCluster()
for idx, row in df2.iterrows():
    mc.add_child(folium.Marker([row['lat'], row['lon']],popup=row['Confirmed']))
m.add_child(mc)

# Display the map
m

Make this Notebook Trusted to load map: File -> Trust Notebook

from folium.plugins import HeatMap

df2

	Country	Confirmed	Recovered	Deaths	lat	lon
0	Afghanistan	56226	49937	2467	33.768006	66.238514
1	Albania	122767	87760	2184	41.000028	19.999962
2	Algeria	116543	81065	3071	28.000027	2.999983
3	Andorra	11687	11111	114	42.540717	1.573203
4	Angola	21914	20190	532	-11.877577	17.569124
...	...	...	...	...	...	...
187	Vietnam	2579	2265	35	13.290403	108.426511
188	West Bank and Gaza	232038	205289	2521	31.904966	35.202341
189	Yemen	3816	1580	810	16.347124	47.891527
190	Zambia	87318	83895	1191	-14.518912	27.558988
191	Zimbabwe	36778	34555	1518	-19.016880	29.353650

192 rows × 6 columns

# Create map with overall cases registered
m = folium.Map(location=[54,15], zoom_start=2)
HeatMap(data=df2[['lat', 'lon','Confirmed']], radius=15).add_to(m)

# Show the map
m

Make this Notebook Trusted to load map: File -> Trust Notebook